Computer-implemented physics-based models are being increasingly used to predict the performance of engineered systems. The use of these performance models is especially important when real world testing is prohibitive.
One type of physics-based modeling system performs probabilistic analysis of engineered systems. Probabilistic algorithms are combined with general-purpose numerical analysis methods to compute the probabilistic response of structural or mechanical components. The system is modeled and uncertainty in loading, material properties, geometry, boundary conditions and initial conditions can be simulated. This performance modeling allows the reliability of the system to be predicted along with identifying important variables contributing to the reliability.
An example of a probabilistic performance modeling system is the NESSUS® probabilistic analysis software, initially developed by Southwest Research Institute for NASA to perform probabilistic analysis of space shuttle main engine components. NESSUS is now applied to a diverse range of problems including aerospace structures, automotive structures, biomechanics, gas turbine engines, geomechanics, nuclear waste packaging, offshore structures, pipelines, and rotordynamics.
Examples of uncertainties in engineered systems are loadings, environmental conditions, material strength, geometry, and manufacturing and assembly variations. In many cases, these uncertainties are not direct physics-based model parameters. For example, variations in the torque of a nut during assembly may be modeled as an initial penetration between two parts of the finite element model. Therefore, intermediate relationships between physical uncertainties and the physics-based model are required.